Batch processes are used in industrial processes for a variety of industries (e.g., the food and chemical industries). A “batch process”, as used herein, refers to a process that runs for a finite duration to produce a batch of product at the end of the duration. The antithesis of a batch process is a continuous process, where raw materials are continuously fed into operation and products are continuously withdrawn from the process. An example of a continuous process is a distillation column. Batch processes are often used in industrial processes for manufacturing batches of specialty products with high added value. Such specialty products include, but are not limited to, pharmaceuticals, resins, and composites. Batch processes are also typically used in industrial processes for producing batches of food. Online monitoring of such batch processes is important for safe and cost effective production of high quality products.
The inherent time varying nature of batch processes results in a variation of batch conditions throughout the duration of the batch process. The phrase “batch condition”, as used herein, refers to the state (or health) of the product being manufactured during a batch process. The state (or health) of a batch operation for making a product can generally be defined in terms of normalcy. For example, a batch condition can indicate a healthy batch of product (i.e., a normal batch of product) or an unhealthy batch of product (i.e., an abnormal batch of product).
There are several methods known in the art for online monitoring of batch processes. These conventional methods are generally oriented towards classifying a batch run as normal or abnormal with a view to provide alarms if the operation is abnormal. These conventional methods also help towards mitigation steps so as to correct an abnormal batch run and bring it back to normal operation (whenever such steps are feasible in practice).
Conventional process monitoring methods generally involve assessing a batch condition at a particular time during the evolution of a batch process. This assessment generally involves: (a) collecting data of the measured variables obtained during the performance of the batch process (i.e., from a start time t0 to the particular time t); (b) considering each measured variable as a distinct variable; (c) considering a set of measured variables as a collection of variables; and (d) representing the collection of variables as a single vector V. The vector V computation requires the complete batch history, which presents a challenge for online assessment of the state of the batch since the set of measured variables are not fully obtained until the batch process is complete. As a result, the assessment requires the forecasting of future variable measurements. The forecasting of future variable measurements generally requires filling up unobserved data related to the unperformed portion of the batch process with historical data, i.e., data obtained during a previous performance of a batch process. This forecasting process ensures that the batch conditions of the product being manufactured are compared to archived batch conditions of manufactured products. In effect, the health of the product being manufactured is assessed in a statistical manner.
However, there are no methods known in the art that are oriented towards performance monitoring of batch runs. Performance monitoring can be logically conceived as the next step after process monitoring to further classify normal batch runs in terms of a set of performance criteria. Performance monitoring in real time during the batch run facilitates an early characterization of the batch run in terms of its performance (qualified in terms of best, good, and average performance) and enables the initiation of remedial measures to improve the batch process performance (whenever necessary and possible). Such steps would help in generating a set of batch runs that have relatively minimal variation amongst them. Such steps also facilitate a shift in the performance of batch runs closer towards the specification on quality. In effect, the steps can result in substantial improvement in manufacturing efficiencies of batch runs.
The conventional process monitoring methods for online monitoring of batch processes described above suffer from certain drawbacks, which prevent these process monitoring methods from being used for performance monitoring. Online process monitoring usually compares the time trajectory of variables in an on-going batch with the trajectories from the archived measurement set which have been classified as normal. Whenever an abnormality occurs in a batch process, the time trajectories of the variables can change substantially. Also, the time trajectories are dissimilar when compared with the trajectories in the archived set. Therefore, abnormalities can be easily resolved in a reasonable time and abnormality conditions can be flagged in a reasonable time. On the other hand, online performance monitoring requires a comparison of variable trajectories that could be similar (but not the same) to the trajectories towards a clearer batch classification on the basis of performance. Improved methods and algorithms that provide a sharper resolution between the time trajectories need to be developed and deployed.